System

Abstract

An object of a system according to an embodiment is to easily check a place where an outdoor advertisement is placed and a price thereof and to grasp an image after the advertisement is placed.SOLUTION: A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives designation of an advertisement place from a user. The collection unit collects the information of the advertisement space based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and proposes an optimal advertisement plan. The generation unit generates an advertisement image based on the advertisement plan proposed by the analysis unit. The providing unit provides the advertisement image generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to easily find out where and how much an outdoor advertisement would cost, making it difficult to get a sense of what the advertisement would look like after it was placed.

[0005] The system according to the embodiment aims to easily check the location and price of outdoor advertising and to get an idea of ​​what the advertising will look like after it is placed. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's designation of an advertisement placement location. The collection unit collects information on advertising space based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and proposes an optimal advertising plan. The generation unit generates an advertising image based on the advertising plan proposed by the analysis unit. The provision unit provides the user with the advertising image generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily check the location and price of outdoor advertising and get an idea of ​​what the advertising will look like after it is placed. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An advertising support system according to an embodiment of the present invention allows a user to easily specify an advertising location, receive a proposal for an optimal advertising plan, and visually confirm the advertising image. The advertising support system accepts a user's designation of an advertising location, collects information about advertising space, proposes an optimal advertising plan, generates an advertising image, and provides it to the user. For example, the advertising support system allows a user to specify a location where they would like to place an advertisement. For example, the user may select an advertising space on the wall of a specific building or at a bus stop. This information is input into the system. The system then collects information about the specified location, such as the availability and price of the advertising space at that location, and past advertising performance data. This data is analyzed by AI and used to propose the optimal advertising plan. Furthermore, the system uses AR technology to provide the user with an image of what the advertisement would look like when placed in the specified location. The user can use a smartphone or tablet to check how the advertisement would appear in a real-world setting. This allows the user to specifically understand what the advertisement would look like when placed. If the user is satisfied with the advertising plan, they can make an inquiry to the advertising contractor through the system. If the inquiry leads to a contract, the system operator monetizes the contract by receiving advertising fees and contract fees. This system allows users to easily research outdoor advertising contractors and prices, and to get a concrete idea of ​​what they want to achieve when placing an ad. Furthermore, the system operator can increase revenue by receiving advertising fees and commissions on successful contracts. This allows the advertising support system to easily specify the location where the user wants to place an ad, have the optimal advertising plan proposed, and visually confirm the ad image. For example, the system can specify the location where the user wants to place an ad, collect information on the advertising space in that location, propose the optimal advertising plan, generate an ad image, and provide it to the user. This allows the user to get a concrete idea of ​​what they want to achieve when placing an ad, and make inquiries to advertising contractors. This allows the system operator to increase revenue by receiving advertising fees and commissions on successful contracts.

[0029] An advertising support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's designation of an advertising location. The user can select, for example, an advertising space on the wall of a specific building or at a bus stop. The collection unit collects information about the advertising space based on the information received by the reception unit. The collection unit collects information such as the availability of the advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The analysis unit analyzes the information collected by the collection unit and proposes an optimal advertising plan. The analysis unit, for example, analyzes the collected data using AI and proposes an optimal advertising plan. The generation unit generates an advertising image based on the advertising plan proposed by the analysis unit. The generation unit generates the advertising image using, for example, AR technology. The provision unit provides the advertising image generated by the generation unit to the user. The provision unit displays the advertising image superimposed on a real landscape using, for example, a smartphone or tablet. As a result, the advertising support system according to the embodiment allows the user to easily specify an advertising placement location, receive a proposal for an optimal advertising plan, and visually confirm the advertising image.

[0030] The collection unit can collect information including the availability of advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The collection unit, for example, collects the availability of advertising space. The availability includes, for example, reservation status and vacancy status. The collection unit, for example, collects the price of advertising space. The price includes, for example, time period, location, type of advertisement, etc. The collection unit, for example, collects past advertising performance. The past advertising performance includes, for example, the number of clicks and conversion rate, etc. The collection unit, for example, collects surrounding traffic volume. The surrounding traffic volume includes, for example, peak traffic volume and average traffic volume, etc. The collection unit, for example, collects visibility. The visibility includes, for example, viewing distance and viewing angle, etc. By collecting detailed information about advertising space, it is possible to propose an optimal advertising plan.

[0031] The generation unit can generate the advertising image using AR technology. The generation unit generates the advertising image using, for example, marker-type AR technology. Marker-type AR technology is a technology that uses a specific marker to overlay the advertising image on a real landscape. The generation unit generates the advertising image using, for example, markerless AR technology. Markerless AR technology is a technology that overlays the advertising image on a real landscape without using a specific marker. The generation unit generates the advertising image using, for example, AR technology and provides it to the user. In this way, by using AR technology, the advertising image can be overlaid on the real landscape and displayed.

[0032] The providing unit can superimpose an advertisement image on a real landscape using a smartphone or a tablet. The providing unit, for example, uses an iOS device to superimpose an advertisement image on a real landscape. Examples of iOS devices include an iPhone (registered trademark) and an iPad (registered trademark). The providing unit, for example, uses an Android device to superimpose an advertisement image on a real landscape. Examples of Android devices include an Android smartphone and an Android tablet. The providing unit, for example, uses a smartphone or a tablet to superimpose an advertisement image on a real landscape. This allows a user to check the advertisement image using a smartphone or a tablet.

[0033] The providing unit may provide a function that allows a user to adjust the size and position of an advertisement. The providing unit may provide a function that allows a user to adjust the size and position of an advertisement, for example, in pixel units. The user may adjust the size and position of an advertisement, for example, by dragging and dropping. The providing unit may provide, for example, a slider for adjusting the size and position of an advertisement. The user may adjust the size and position of an advertisement using the slider. The providing unit may provide, for example, an input field for adjusting the size and position of an advertisement. The user may adjust the size and position of an advertisement by, for example, inputting a numerical value into the input field. This allows the user to freely adjust the size and position of an advertisement.

[0034] The providing unit can provide a function for making an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with. The providing unit provides, for example, a function for making an inquiry to an advertising agency. An advertising agency includes, for example, a company that produces and distributes advertisements. The providing unit provides, for example, a function for making an inquiry to a freelance designer. A freelance designer includes, for example, a designer who designs advertisements individually. The providing unit provides, for example, a function for making an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with. This allows the user to make an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with.

[0035] The reception unit can analyze the user's past advertising history and suggest the optimal designation method. For example, the reception unit automatically displays as candidates advertising locations that the user has frequently designated in the past. For example, the reception unit preferentially suggests designation methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests advertising locations to be used in specific time periods based on the user's past advertising history. This makes it possible to suggest the optimal designation method based on the user's past advertising history.

[0036] When specifying an advertising location, the reception unit can perform filtering based on the user's current project or area of ​​interest. For example, the reception unit preferentially displays advertising locations related to a project currently underway by the user. For example, the reception unit suggests highly relevant advertising locations based on the user's area of ​​interest. For example, the reception unit analyzes the user's past project history and suggests the optimal advertising location. This makes it possible to suggest the optimal advertising location based on the user's current project or area of ​​interest.

[0037] When specifying an advertisement placement location, the reception unit can select an appropriate designation means according to the user's input method. For example, when a user specifies an advertisement placement location by voice, the reception unit supports the input using voice recognition technology. For example, when a user specifies an advertisement placement location by text, the reception unit preferentially supports text input. For example, when a user specifies an advertisement placement location by image, the reception unit supports the input using image recognition technology. This makes it possible to select the optimal designation means according to the user's input method.

[0038] When specifying an advertisement placement location, the reception unit can prioritize specifying a highly relevant location in consideration of the user's geographical location information. The reception unit, for example, prioritizes suggesting an advertisement placement location close to the user's current location. The reception unit, for example, suggests a highly relevant advertisement placement location based on the user's past movement history. The reception unit, for example, suggests an optimal advertisement placement location based on the user's geographical location information. This makes it possible to suggest an optimal advertisement placement location in consideration of the user's geographical location information.

[0039] When specifying an advertisement placement location, the reception unit can analyze the user's social media activity and specify related locations. For example, the reception unit suggests locations where the user has checked in on social media as advertisement placement locations. For example, the reception unit analyzes the content of the user's social media posts and suggests related advertisement placement locations. For example, the reception unit suggests related advertisement placement locations based on the activity of the user's friends on social media. This makes it possible to suggest optimal advertisement placement locations based on the user's social media activity.

[0040] The reception unit can customize the designation method by reflecting the user's past feedback when designating an advertisement placement location. The reception unit, for example, suggests an optimal advertisement placement location based on feedback provided by the user in the past. The reception unit, for example, analyzes the user's past feedback and customizes the designation method. The reception unit, for example, optimizes the designation procedure for an advertisement placement location based on the user's feedback history. This makes it possible to propose an optimal designation method based on the user's past feedback.

[0041] The collection unit can refer to the user's past advertising history when collecting information such as the availability of advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The collection unit, for example, prioritizes collecting information on advertising space that the user has used in the past. The collection unit, for example, collects information on optimal advertising space based on the user's past advertising history. The collection unit, for example, analyzes the user's past advertising history and collects information on highly relevant advertising spaces. This makes it possible to collect information on optimal advertising space based on the user's past advertising history.

[0042] When collecting information on advertising spaces, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit preferentially collects information on advertising spaces related to a project currently underway by the user. For example, the collection unit collects information on highly relevant advertising spaces based on the user's area of ​​interest. For example, the collection unit analyzes the user's past project history to collect information on optimal advertising spaces. This makes it possible to collect information on optimal advertising spaces based on the user's current project or area of ​​interest.

[0043] When collecting information about advertising spaces, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user collects information about advertising spaces by voice, the collection unit supports input using voice recognition technology. For example, when the user collects information about advertising spaces by text, the collection unit preferentially supports text input. For example, when the user collects information about advertising spaces by image, the collection unit supports input using image recognition technology. This makes it possible to select the optimal collection means depending on the user's input method.

[0044] When collecting information on advertising spaces, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information. The collection unit, for example, prioritizes collecting information on advertising spaces close to the user's current location. The collection unit, for example, collects information on highly relevant advertising spaces based on the user's past movement history. The collection unit, for example, collects information on optimal advertising spaces based on the user's geographical location information. This makes it possible to collect information on optimal advertising spaces based on the user's geographical location information.

[0045] When collecting information on advertising spaces, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information on advertising spaces at locations where the user has checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects information on related advertising spaces. For example, the collection unit collects information on related advertising spaces by referring to the activities of the user's friends on social media. This makes it possible to collect information on optimal advertising spaces based on the user's social media activities.

[0046] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information on advertising spaces. For example, the collection unit collects information on optimal advertising spaces based on feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and customizes the collection method. For example, the collection unit optimizes the procedure for collecting information on advertising spaces based on the user's feedback history. This makes it possible to propose the optimal collection method based on the user's past feedback.

[0047] The analysis unit can refer to the user's past advertising history when analyzing the information on the advertising space. The analysis unit, for example, analyzes the optimal advertising plan based on information on advertising spaces used by the user in the past. The analysis unit, for example, analyzes the optimal advertising plan based on the user's past advertising history. The analysis unit, for example, analyzes the user's past advertising history and analyzes highly relevant advertising plans. This makes it possible to analyze the optimal advertising plan based on the user's past advertising history.

[0048] When analyzing information about the advertising space, the analysis unit can filter based on the user's current project or area of ​​interest. For example, the analysis unit prioritizes analysis of advertising plans related to the user's current ongoing project. For example, the analysis unit analyzes highly relevant advertising plans based on the user's area of ​​interest. For example, the analysis unit analyzes the user's past project history to analyze the optimal advertising plan. This makes it possible to analyze the optimal advertising plan based on the user's current project or area of ​​interest.

[0049] When analyzing the information on the advertising space, the analysis unit can select the optimal analysis means depending on the user's input method. For example, when the user analyzes the information on the advertising space using voice, the analysis unit supports the analysis using voice recognition technology. For example, when the user analyzes the information on the advertising space using text, the analysis unit preferentially uses text analysis technology. For example, when the user analyzes the information on the advertising space using an image, the analysis unit supports the analysis using image recognition technology. This allows the optimal analysis means to be selected depending on the user's input method.

[0050] When analyzing information about advertising spaces, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. The analysis unit, for example, prioritizes analysis of information about advertising spaces close to the user's current location. The analysis unit, for example, analyzes information about highly relevant advertising spaces based on the user's past movement history. The analysis unit, for example, analyzes information about optimal advertising spaces based on the user's geographical location information. This makes it possible to analyze information about optimal advertising spaces based on the user's geographical location information.

[0051] When analyzing the information on advertising spaces, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit analyzes information on advertising spaces at locations where the user has checked in on social media. For example, the analysis unit analyzes the content of the user's posts on social media and analyzes information on related advertising spaces. For example, the analysis unit analyzes information on related advertising spaces with reference to the activities of the user's friends on social media. This makes it possible to analyze information on optimal advertising spaces based on the user's social media activity.

[0052] When analyzing information about advertising spaces, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit analyzes information about optimal advertising spaces, for example, based on feedback provided by the user in the past. The analysis unit analyzes, for example, the user's past feedback and customizes the analysis method. The analysis unit optimizes the procedure for analyzing information about advertising spaces, for example, based on the user's feedback history. This makes it possible to propose an optimal analysis method based on the user's past feedback.

[0053] The generation unit can refer to the user's past advertising history when generating an advertising image. The generation unit generates an optimal advertising image, for example, based on advertising images used by the user in the past. The generation unit generates an optimal advertising image, for example, based on the user's past advertising history. The generation unit analyzes the user's past advertising history, for example, and generates a highly relevant advertising image. This makes it possible to generate an optimal advertising image based on the user's past advertising history.

[0054] The generation unit may perform filtering based on the user's current project or area of ​​interest when generating advertisement images. For example, the generation unit may preferentially generate advertisement images related to a project currently underway by the user. For example, the generation unit may generate highly relevant advertisement images based on the user's area of ​​interest. For example, the generation unit may analyze the user's past project history to generate optimal advertisement images. This allows optimal advertisement images to be generated based on the user's current project or area of ​​interest.

[0055] The generation unit can select an optimal generation means depending on a user's input method when generating an advertisement image. For example, when a user generates an advertisement image using voice, the generation unit supports the generation using voice recognition technology. For example, when a user generates an advertisement image using text, the generation unit preferentially uses text generation technology. For example, when a user generates an advertisement image using an image, the generation unit supports the generation using image recognition technology. This allows the optimal generation means to be selected depending on a user's input method.

[0056] When generating an advertisement image, the generation unit can prioritize generating a highly relevant image in consideration of the user's geographical location information. For example, the generation unit prioritizes generating an advertisement image that is close to the user's current location. For example, the generation unit generates a highly relevant advertisement image based on the user's past movement history. For example, the generation unit generates an optimal advertisement image based on the user's geographical location information. This makes it possible to generate an optimal advertisement image based on the user's geographical location information.

[0057] The generation unit may analyze the user's social media activity and generate a related image when generating an advertisement image. For example, the generation unit generates an advertisement image for a location where the user has checked in on social media. For example, the generation unit may analyze the content posted by the user on social media to generate a related advertisement image. For example, the generation unit may generate a related advertisement image by referring to the activity of the user's friends on social media. In this way, an optimal advertisement image can be generated based on the user's social media activity.

[0058] The generation unit can customize the generation method by reflecting the user's past feedback when generating an advertisement image. The generation unit generates an optimal advertisement image based on, for example, feedback provided by the user in the past. The generation unit customizes the generation method by, for example, analyzing the user's past feedback. The generation unit optimizes the advertisement image generation procedure based on, for example, the user's feedback history. This makes it possible to propose an optimal generation method based on the user's past feedback.

[0059] The provision unit can refer to the user's past advertising history when providing an advertising image. The provision unit, for example, proposes an optimal provision method based on advertising images used by the user in the past. The provision unit, for example, proposes an optimal provision method based on the user's past advertising history. The provision unit, for example, analyzes the user's past advertising history and proposes a highly relevant provision method. This makes it possible to propose an optimal provision method based on the user's past advertising history.

[0060] The providing unit may perform filtering based on the user's current project or area of ​​interest when providing advertisement images. For example, the providing unit may preferentially provide advertisement images related to a project currently underway by the user. For example, the providing unit may provide highly relevant advertisement images based on the user's area of ​​interest. For example, the providing unit may analyze the user's past project history and provide optimal advertisement images. This allows the optimal advertisement images to be provided based on the user's current project or area of ​​interest.

[0061] The providing unit can select the optimal providing means depending on the user's input method when providing an advertisement image. For example, when the user provides the advertisement image by voice, the providing unit supports the providing using voice recognition technology. For example, when the user provides the advertisement image by text, the providing unit preferentially uses text providing technology. For example, when the user provides the advertisement image by image, the providing unit supports the providing using image recognition technology. This makes it possible to select the optimal providing means depending on the user's input method.

[0062] When providing advertisement images, the providing unit can provide highly relevant images preferentially in consideration of the user's geographical location information. For example, the providing unit can provide advertisement images closer to the user's current location preferentially. For example, the providing unit can provide highly relevant advertisement images based on the user's past movement history. For example, the providing unit can provide the optimal advertisement image based on the user's geographical location information. This makes it possible to provide the optimal advertisement image based on the user's geographical location information.

[0063] The providing unit may analyze the user's social media activity and provide a related image when providing an advertisement image. For example, the providing unit may provide an advertisement image of a place where the user has checked in on social media. For example, the providing unit may analyze the content posted by the user on social media and provide a related advertisement image. For example, the providing unit may provide a related advertisement image by referring to the activity of the user's friends on social media. This allows the optimal advertisement image to be provided based on the user's social media activity.

[0064] The providing unit can customize the providing method by reflecting the user's past feedback when providing an advertisement image. The providing unit provides an optimal advertisement image based on, for example, feedback provided by the user in the past. The providing unit, for example, analyzes the user's past feedback and customizes the providing method. The providing unit, for example, optimizes the procedure for providing advertisement images based on the user's feedback history. This makes it possible to propose an optimal providing method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The advertising support system can further include a budget management unit that proposes an optimal advertising plan taking into account the user's advertising budget. The budget management unit, for example, proposes advertising space that will achieve the maximum effect within the budget based on the advertising budget set by the user. The budget management unit, for example, compares the price and effect of advertising space and proposes an advertising plan with high cost performance. The budget management unit, for example, analyzes the user's past advertising budget and performance and proposes the optimal budget allocation. This allows the user to select an advertising plan that will achieve the maximum effect within the budget.

[0067] The collection unit can further collect environmental sounds and surrounding noise levels around the advertising space. For example, the collection unit measures the noise level around the advertising space to evaluate the visibility and auditory impact of the advertisement. For example, the collection unit analyzes the acoustic environment around the advertising space to predict the effectiveness of the audio advertisement. For example, the collection unit collects acoustic data around the advertising space and reflects it in the design and placement of the advertisement. This can provide detailed environmental information to maximize the effectiveness of the advertisement.

[0068] The providing unit can further customize the display method of the advertisement image based on the user's visual preferences. For example, the providing unit displays the advertisement image using colors and fonts preferred by the user. For example, the providing unit provides the advertisement image that reflects the user's preferred layout and design style. For example, the providing unit analyzes the user's past visual preference data and suggests an optimal display method. In this way, it is possible to provide the advertisement image according to the user's visual preferences.

[0069] The reception unit can further propose the optimal advertising location taking into consideration the user's advertising purpose. For example, if the user's goal is brand awareness, the reception unit proposes an advertising space with high visibility. For example, if the user's goal is sales promotion, the reception unit proposes an advertising space that attracts a large number of target demographics. For example, if the user's goal is event announcement, the reception unit proposes an advertising space around the event venue. This makes it possible to propose the optimal advertising location according to the user's advertising purpose.

[0070] The collection unit can further collect weather information around the advertising space. For example, the collection unit collects weather data around the advertising space and evaluates the visibility and effectiveness of the advertisement. For example, the collection unit proposes the optimal timing for placing an advertisement based on a weather forecast around the advertising space. For example, the collection unit analyzes weather data around the advertising space and reflects the data in the design and placement of the advertisement. This makes it possible to propose an optimal advertising plan that takes weather information into consideration.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives a user's designation of the location where the advertisement will be placed. For example, the user can select an advertising space on the wall of a specific building or at a bus stop. Step 2: The collection unit collects information about the advertising space based on the information received by the reception unit. The collection unit collects information such as the availability of the advertising space, price, past advertising performance, surrounding traffic volume, visibility, etc. Step 3: The analysis unit analyzes the information collected by the collection unit and proposes the optimal advertising plan. For example, the analysis unit uses AI to analyze the collected data and proposes the optimal advertising plan. Step 4: The generation unit generates an advertising image based on the advertising plan proposed by the analysis unit. For example, the generation unit generates the advertising image using AR technology. Step 5: The providing unit provides the advertisement image generated by the generating unit to the user. The providing unit displays the advertisement image by superimposing it on a real landscape using, for example, a smartphone or a tablet.

[0073] (Example 2) An advertising support system according to an embodiment of the present invention allows a user to easily specify an advertising location, receive a proposal for an optimal advertising plan, and visually confirm the advertising image. The advertising support system accepts a user's designation of an advertising location, collects information about advertising space, proposes an optimal advertising plan, generates an advertising image, and provides it to the user. For example, the advertising support system allows a user to specify a location where they would like to place an advertisement. For example, the user may select an advertising space on the wall of a specific building or at a bus stop. This information is input into the system. The system then collects information about the specified location, such as the availability and price of the advertising space at that location, and past advertising performance data. This data is analyzed by AI and used to propose the optimal advertising plan. Furthermore, the system uses AR technology to provide the user with an image of what the advertisement would look like when placed in the specified location. The user can use a smartphone or tablet to check how the advertisement would appear in a real-world setting. This allows the user to specifically understand what the advertisement would look like when placed. If the user is satisfied with the advertising plan, they can make an inquiry to the advertising contractor through the system. If the inquiry leads to a contract, the system operator monetizes the contract by receiving advertising fees and contract fees. This system allows users to easily research outdoor advertising contractors and prices, and to get a concrete idea of ​​what they want to achieve when placing an ad. Furthermore, the system operator can increase revenue by receiving advertising fees and commissions on successful contracts. This allows the advertising support system to easily specify the location where the user wants to place an ad, have the optimal advertising plan proposed, and visually confirm the ad image. For example, the system can specify the location where the user wants to place an ad, collect information on the advertising space in that location, propose the optimal advertising plan, generate an ad image, and provide it to the user. This allows the user to get a concrete idea of ​​what they want to achieve when placing an ad, and make inquiries to advertising contractors. This allows the system operator to increase revenue by receiving advertising fees and commissions on successful contracts.

[0074] An advertising support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's designation of an advertising location. The user can select, for example, an advertising space on the wall of a specific building or at a bus stop. The collection unit collects information about the advertising space based on the information received by the reception unit. The collection unit collects information such as the availability of the advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The analysis unit analyzes the information collected by the collection unit and proposes an optimal advertising plan. The analysis unit, for example, analyzes the collected data using AI and proposes an optimal advertising plan. The generation unit generates an advertising image based on the advertising plan proposed by the analysis unit. The generation unit generates the advertising image using, for example, AR technology. The provision unit provides the advertising image generated by the generation unit to the user. The provision unit displays the advertising image superimposed on a real landscape using, for example, a smartphone or tablet. As a result, the advertising support system according to the embodiment allows the user to easily specify an advertising placement location, receive a proposal for an optimal advertising plan, and visually confirm the advertising image.

[0075] The collection unit can collect information including the availability of advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The collection unit, for example, collects the availability of advertising space. The availability includes, for example, reservation status and vacancy status. The collection unit, for example, collects the price of advertising space. The price includes, for example, time period, location, type of advertisement, etc. The collection unit, for example, collects past advertising performance. The past advertising performance includes, for example, the number of clicks and conversion rate, etc. The collection unit, for example, collects surrounding traffic volume. The surrounding traffic volume includes, for example, peak traffic volume and average traffic volume, etc. The collection unit, for example, collects visibility. The visibility includes, for example, viewing distance and viewing angle, etc. By collecting detailed information about advertising space, it is possible to propose an optimal advertising plan.

[0076] The generation unit can generate the advertising image using AR technology. The generation unit generates the advertising image using, for example, marker-type AR technology. Marker-type AR technology is a technology that uses a specific marker to overlay the advertising image on a real landscape. The generation unit generates the advertising image using, for example, markerless AR technology. Markerless AR technology is a technology that overlays the advertising image on a real landscape without using a specific marker. The generation unit generates the advertising image using, for example, AR technology and provides it to the user. In this way, by using AR technology, the advertising image can be overlaid on the real landscape and displayed.

[0077] The providing unit can superimpose an advertisement image on a real landscape using a smartphone or a tablet. The providing unit, for example, uses an iOS device to superimpose the advertisement image on a real landscape. Examples of iOS devices include an iPhone and an iPad. The providing unit, for example, uses an Android device to superimpose the advertisement image on a real landscape. Examples of Android devices include an Android smartphone and an Android tablet. The providing unit, for example, uses a smartphone or a tablet to superimpose the advertisement image on a real landscape. This allows a user to check the advertisement image using a smartphone or a tablet.

[0078] The providing unit may provide a function that allows a user to adjust the size and position of an advertisement. The providing unit may provide a function that allows a user to adjust the size and position of an advertisement, for example, in pixel units. The user may adjust the size and position of an advertisement, for example, by dragging and dropping. The providing unit may provide, for example, a slider for adjusting the size and position of an advertisement. The user may adjust the size and position of an advertisement using the slider. The providing unit may provide, for example, an input field for adjusting the size and position of an advertisement. The user may adjust the size and position of an advertisement by, for example, inputting a numerical value into the input field. This allows the user to freely adjust the size and position of an advertisement.

[0079] The providing unit can provide a function for making an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with. The providing unit provides, for example, a function for making an inquiry to an advertising agency. An advertising agency includes, for example, a company that produces and distributes advertisements. The providing unit provides, for example, a function for making an inquiry to a freelance designer. A freelance designer includes, for example, a designer who designs advertisements individually. The providing unit provides, for example, a function for making an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with. This allows the user to make an inquiry to an advertising contractor based on an advertising plan that the user is satisfied with.

[0080] The reception unit can estimate the user's emotions and adjust the method for specifying the advertisement placement location based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the steps for specifying the advertisement placement location. For example, when the user is relaxed, the reception unit provides detailed specification options and suggests a customizable specification method. For example, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly specify the advertisement placement location. This makes it possible to adjust the method for specifying the advertisement placement location according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The reception unit can analyze the user's past advertising history and suggest the optimal designation method. For example, the reception unit automatically displays as candidates advertising locations that the user has frequently designated in the past. For example, the reception unit preferentially suggests designation methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests advertising locations to be used in specific time periods based on the user's past advertising history. This makes it possible to suggest the optimal designation method based on the user's past advertising history.

[0082] When specifying an advertising location, the reception unit can perform filtering based on the user's current project or area of ​​interest. For example, the reception unit preferentially displays advertising locations related to a project currently underway by the user. For example, the reception unit suggests highly relevant advertising locations based on the user's area of ​​interest. For example, the reception unit analyzes the user's past project history and suggests the optimal advertising location. This makes it possible to suggest the optimal advertising location based on the user's current project or area of ​​interest.

[0083] When specifying an advertisement placement location, the reception unit can select an appropriate designation means according to the user's input method. For example, when a user specifies an advertisement placement location by voice, the reception unit supports the input using voice recognition technology. For example, when a user specifies an advertisement placement location by text, the reception unit preferentially supports text input. For example, when a user specifies an advertisement placement location by image, the reception unit supports the input using image recognition technology. This makes it possible to select the optimal designation means according to the user's input method.

[0084] The reception unit can estimate the user's emotions and determine the priority of the advertisement placement locations to be specified based on the estimated user emotions. For example, when the user is excited, the reception unit preferentially suggests visually attractive advertisement placement locations. For example, when the user is relaxed, the reception unit preferentially suggests advertisement placement locations that include detailed information. For example, when the user is stressed, the reception unit preferentially suggests advertisement placement locations that are simple and easy to understand. This allows the priority of advertisement placement locations to be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When specifying an advertisement placement location, the reception unit can prioritize specifying a highly relevant location in consideration of the user's geographical location information. The reception unit, for example, prioritizes suggesting an advertisement placement location close to the user's current location. The reception unit, for example, suggests a highly relevant advertisement placement location based on the user's past movement history. The reception unit, for example, suggests an optimal advertisement placement location based on the user's geographical location information. This makes it possible to suggest an optimal advertisement placement location in consideration of the user's geographical location information.

[0086] When specifying an advertisement placement location, the reception unit can analyze the user's social media activity and specify related locations. For example, the reception unit suggests locations where the user has checked in on social media as advertisement placement locations. For example, the reception unit analyzes the content of the user's social media posts and suggests related advertisement placement locations. For example, the reception unit suggests related advertisement placement locations based on the activity of the user's friends on social media. This makes it possible to suggest optimal advertisement placement locations based on the user's social media activity.

[0087] The reception unit can customize the designation method by reflecting the user's past feedback when designating an advertisement placement location. The reception unit, for example, suggests an optimal advertisement placement location based on feedback provided by the user in the past. The reception unit, for example, analyzes the user's past feedback and customizes the designation method. The reception unit, for example, optimizes the designation procedure for an advertisement placement location based on the user's feedback history. This makes it possible to propose an optimal designation method based on the user's past feedback.

[0088] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit prioritizes collecting information on visually attractive advertising spaces. For example, when the user is relaxed, the collection unit prioritizes collecting information on advertising spaces that include detailed information. For example, when the user is stressed, the collection unit prioritizes collecting information on simple and easy-to-understand advertising spaces. This makes it possible to determine the priority of information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The collection unit can refer to the user's past advertising history when collecting information such as the availability of advertising space, price, past advertising performance, surrounding traffic volume, and visibility. The collection unit, for example, prioritizes collecting information on advertising space that the user has used in the past. The collection unit, for example, collects information on optimal advertising space based on the user's past advertising history. The collection unit, for example, analyzes the user's past advertising history and collects information on highly relevant advertising spaces. This makes it possible to collect information on optimal advertising space based on the user's past advertising history.

[0090] When collecting information on advertising spaces, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit preferentially collects information on advertising spaces related to a project currently underway by the user. For example, the collection unit collects information on highly relevant advertising spaces based on the user's area of ​​interest. For example, the collection unit analyzes the user's past project history to collect information on optimal advertising spaces. This makes it possible to collect information on optimal advertising spaces based on the user's current project or area of ​​interest.

[0091] When collecting information about advertising spaces, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user collects information about advertising spaces by voice, the collection unit supports input using voice recognition technology. For example, when the user collects information about advertising spaces by text, the collection unit preferentially supports text input. For example, when the user collects information about advertising spaces by image, the collection unit supports input using image recognition technology. This makes it possible to select the optimal collection means depending on the user's input method.

[0092] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. For example, when the user is excited, the collection unit provides a visually appealing display method. For example, when the user is relaxed, the collection unit provides a display method including detailed information. For example, when the user is stressed, the collection unit provides a simple and easy-to-understand display method. This makes it possible to adjust the display method of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] When collecting information on advertising spaces, the collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information. The collection unit, for example, prioritizes collecting information on advertising spaces close to the user's current location. The collection unit, for example, collects information on highly relevant advertising spaces based on the user's past movement history. The collection unit, for example, collects information on optimal advertising spaces based on the user's geographical location information. This makes it possible to collect information on optimal advertising spaces based on the user's geographical location information.

[0094] When collecting information on advertising spaces, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information on advertising spaces at locations where the user has checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects information on related advertising spaces. For example, the collection unit collects information on related advertising spaces by referring to the activities of the user's friends on social media. This makes it possible to collect information on optimal advertising spaces based on the user's social media activities.

[0095] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information on advertising spaces. For example, the collection unit collects information on optimal advertising spaces based on feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and customizes the collection method. For example, the collection unit optimizes the procedure for collecting information on advertising spaces based on the user's feedback history. This makes it possible to propose the optimal collection method based on the user's past feedback.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is excited, the analysis unit prioritizes analyzing visually appealing advertising plans. For example, if the user is relaxed, the analysis unit prioritizes analyzing advertising plans that include detailed information. For example, if the user is stressed, the analysis unit prioritizes analyzing simple and easy-to-understand advertising plans. This allows the analysis criteria to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The analysis unit can refer to the user's past advertising history when analyzing the information on the advertising space. The analysis unit, for example, analyzes the optimal advertising plan based on information on advertising spaces used by the user in the past. The analysis unit, for example, analyzes the optimal advertising plan based on the user's past advertising history. The analysis unit, for example, analyzes the user's past advertising history and analyzes highly relevant advertising plans. This makes it possible to analyze the optimal advertising plan based on the user's past advertising history.

[0098] When analyzing information about the advertising space, the analysis unit can filter based on the user's current project or area of ​​interest. For example, the analysis unit prioritizes analysis of advertising plans related to the user's current ongoing project. For example, the analysis unit analyzes highly relevant advertising plans based on the user's area of ​​interest. For example, the analysis unit analyzes the user's past project history to analyze the optimal advertising plan. This makes it possible to analyze the optimal advertising plan based on the user's current project or area of ​​interest.

[0099] When analyzing the information on the advertising space, the analysis unit can select the optimal analysis means depending on the user's input method. For example, when the user analyzes the information on the advertising space using voice, the analysis unit supports the analysis using voice recognition technology. For example, when the user analyzes the information on the advertising space using text, the analysis unit preferentially uses text analysis technology. For example, when the user analyzes the information on the advertising space using an image, the analysis unit supports the analysis using image recognition technology. This allows the optimal analysis means to be selected depending on the user's input method.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit provides a visually appealing display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand display method. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] When analyzing information about advertising spaces, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. The analysis unit, for example, prioritizes analysis of information about advertising spaces close to the user's current location. The analysis unit, for example, analyzes information about highly relevant advertising spaces based on the user's past movement history. The analysis unit, for example, analyzes information about optimal advertising spaces based on the user's geographical location information. This makes it possible to analyze information about optimal advertising spaces based on the user's geographical location information.

[0102] When analyzing the information on advertising spaces, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit analyzes information on advertising spaces at locations where the user has checked in on social media. For example, the analysis unit analyzes the content of the user's posts on social media and analyzes information on related advertising spaces. For example, the analysis unit analyzes information on related advertising spaces with reference to the activities of the user's friends on social media. This makes it possible to analyze information on optimal advertising spaces based on the user's social media activity.

[0103] When analyzing information about advertising spaces, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit analyzes information about optimal advertising spaces, for example, based on feedback provided by the user in the past. The analysis unit analyzes, for example, the user's past feedback and customizes the analysis method. The analysis unit optimizes the procedure for analyzing information about advertising spaces, for example, based on the user's feedback history. This makes it possible to propose an optimal analysis method based on the user's past feedback.

[0104] The generation unit can estimate the user's emotions and adjust the advertisement image generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates an advertisement image that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit generates an advertisement image that emphasizes the shortest route. For example, if the user is excited, the generation unit generates an advertisement image that adds a visually stimulating effect. This allows the advertisement image generation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The generation unit can refer to the user's past advertising history when generating an advertising image. The generation unit generates an optimal advertising image, for example, based on advertising images used by the user in the past. The generation unit generates an optimal advertising image, for example, based on the user's past advertising history. The generation unit analyzes the user's past advertising history, for example, and generates a highly relevant advertising image. This makes it possible to generate an optimal advertising image based on the user's past advertising history.

[0106] The generation unit may perform filtering based on the user's current project or area of ​​interest when generating advertisement images. For example, the generation unit may preferentially generate advertisement images related to a project currently underway by the user. For example, the generation unit may generate highly relevant advertisement images based on the user's area of ​​interest. For example, the generation unit may analyze the user's past project history to generate optimal advertisement images. This allows optimal advertisement images to be generated based on the user's current project or area of ​​interest.

[0107] The generation unit can select an optimal generation means depending on a user's input method when generating an advertisement image. For example, when a user generates an advertisement image using voice, the generation unit supports the generation using voice recognition technology. For example, when a user generates an advertisement image using text, the generation unit preferentially uses text generation technology. For example, when a user generates an advertisement image using an image, the generation unit supports the generation using image recognition technology. This allows the optimal generation means to be selected depending on a user's input method.

[0108] The generation unit can estimate the user's emotions and adjust the display method of the generated advertising image based on the estimated user emotions. For example, when the user is excited, the generation unit provides a visually appealing display method. For example, when the user is relaxed, the generation unit provides a display method including detailed information. For example, when the user is stressed, the generation unit provides a simple and easy-to-understand display method. This makes it possible to adjust the display method of the advertising image based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] When generating an advertisement image, the generation unit can prioritize generating a highly relevant image in consideration of the user's geographical location information. For example, the generation unit prioritizes generating an advertisement image that is close to the user's current location. For example, the generation unit generates a highly relevant advertisement image based on the user's past movement history. For example, the generation unit generates an optimal advertisement image based on the user's geographical location information. This makes it possible to generate an optimal advertisement image based on the user's geographical location information.

[0110] The generation unit may analyze the user's social media activity and generate a related image when generating an advertisement image. For example, the generation unit generates an advertisement image for a location where the user has checked in on social media. For example, the generation unit may analyze the content posted by the user on social media to generate a related advertisement image. For example, the generation unit may generate a related advertisement image by referring to the activity of the user's friends on social media. In this way, an optimal advertisement image can be generated based on the user's social media activity.

[0111] The generation unit can customize the generation method by reflecting the user's past feedback when generating an advertisement image. The generation unit generates an optimal advertisement image based on, for example, feedback provided by the user in the past. The generation unit customizes the generation method by, for example, analyzing the user's past feedback. The generation unit optimizes the advertisement image generation procedure based on, for example, the user's feedback history. This makes it possible to propose an optimal generation method based on the user's past feedback.

[0112] The providing unit can estimate the user's emotions and adjust the way in which the advertising image is presented based on the estimated user's emotions. For example, when the user is excited, the providing unit provides a visually appealing presentation method. For example, when the user is relaxed, the providing unit provides a presentation method that includes detailed information. For example, when the user is stressed, the providing unit provides a simple and easy-to-understand presentation method. This makes it possible to adjust the way in which the advertising image is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The provision unit can refer to the user's past advertising history when providing an advertising image. The provision unit, for example, proposes an optimal provision method based on advertising images used by the user in the past. The provision unit, for example, proposes an optimal provision method based on the user's past advertising history. The provision unit, for example, analyzes the user's past advertising history and proposes a highly relevant provision method. This makes it possible to propose an optimal provision method based on the user's past advertising history.

[0114] The providing unit may perform filtering based on the user's current project or area of ​​interest when providing advertisement images. For example, the providing unit may preferentially provide advertisement images related to a project currently underway by the user. For example, the providing unit may provide highly relevant advertisement images based on the user's area of ​​interest. For example, the providing unit may analyze the user's past project history and provide optimal advertisement images. This allows the optimal advertisement images to be provided based on the user's current project or area of ​​interest.

[0115] The providing unit can select the optimal providing means depending on the user's input method when providing an advertisement image. For example, when the user provides the advertisement image by voice, the providing unit supports the providing using voice recognition technology. For example, when the user provides the advertisement image by text, the providing unit preferentially uses text providing technology. For example, when the user provides the advertisement image by image, the providing unit supports the providing using image recognition technology. This makes it possible to select the optimal providing means depending on the user's input method.

[0116] The providing unit can estimate the user's emotions and adjust the display method of the advertising image to be provided based on the estimated user's emotions. For example, when the user is excited, the providing unit provides a visually appealing display method. For example, when the user is relaxed, the providing unit provides a display method including detailed information. For example, when the user is stressed, the providing unit provides a simple and easy-to-understand display method. This makes it possible to adjust the display method of the advertising image based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] When providing advertisement images, the providing unit can provide highly relevant images preferentially in consideration of the user's geographical location information. For example, the providing unit can provide advertisement images closer to the user's current location preferentially. For example, the providing unit can provide highly relevant advertisement images based on the user's past movement history. For example, the providing unit can provide the optimal advertisement image based on the user's geographical location information. This makes it possible to provide the optimal advertisement image based on the user's geographical location information.

[0118] The providing unit may analyze the user's social media activity and provide a related image when providing an advertisement image. For example, the providing unit may provide an advertisement image of a place where the user has checked in on social media. For example, the providing unit may analyze the content posted by the user on social media and provide a related advertisement image. For example, the providing unit may provide a related advertisement image by referring to the activity of the user's friends on social media. This allows the optimal advertisement image to be provided based on the user's social media activity.

[0119] The providing unit can customize the providing method by reflecting the user's past feedback when providing an advertisement image. The providing unit provides an optimal advertisement image based on, for example, feedback provided by the user in the past. The providing unit, for example, analyzes the user's past feedback and customizes the providing method. The providing unit, for example, optimizes the procedure for providing advertisement images based on the user's feedback history. This makes it possible to propose an optimal providing method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a user's designation of an advertisement placement location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on advertisement space. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and proposes an optimal advertisement plan. The generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates an advertisement image using AR technology. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated advertisement image to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a user's designation of an advertisement placement location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on advertisement space. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and proposes an optimal advertisement plan. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates an advertisement image using AR technology. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated advertisement image to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a user's designation of an advertisement placement location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on advertisement spaces. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and proposes an optimal advertisement plan. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and generates an advertisement image using AR technology. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated advertisement image to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a user's designation of an advertisement placement location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on advertisement spaces. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and proposes an optimal advertisement plan. The generation unit is realized, for example, by the control unit 46A of the robot 414 and generates an advertisement image using AR technology. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated advertisement image to the user.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The advertising support system can further include a budget management unit that proposes an optimal advertising plan taking into account the user's advertising budget. The budget management unit, for example, proposes advertising space that will achieve the maximum effect within the budget based on the advertising budget set by the user. The budget management unit, for example, compares the price and effect of advertising space and proposes an advertising plan with high cost performance. The budget management unit, for example, analyzes the user's past advertising budget and performance and proposes the optimal budget allocation. This allows the user to select an advertising plan that will achieve the maximum effect within the budget.

[0122] The collection unit can further collect environmental sounds and surrounding noise levels around the advertising space. For example, the collection unit measures the noise level around the advertising space to evaluate the visibility and auditory impact of the advertisement. For example, the collection unit analyzes the acoustic environment around the advertising space to predict the effectiveness of the audio advertisement. For example, the collection unit collects acoustic data around the advertising space and reflects it in the design and placement of the advertisement. This can provide detailed environmental information to maximize the effectiveness of the advertisement.

[0123] The generation unit can further estimate the user's emotions and adjust the color and design of the advertising image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an advertising image using calming colors and designs. For example, if the user is excited, the generation unit generates an advertising image using vivid colors and dynamic designs. For example, if the user is stressed, the generation unit generates an advertising image using a simple, visually less stressful design. This makes it possible to provide advertising images that correspond to the user's emotions.

[0124] The providing unit can further customize the display method of the advertisement image based on the user's visual preferences. For example, the providing unit displays the advertisement image using colors and fonts preferred by the user. For example, the providing unit provides the advertisement image that reflects the user's preferred layout and design style. For example, the providing unit analyzes the user's past visual preference data and suggests an optimal display method. In this way, it is possible to provide the advertisement image according to the user's visual preferences.

[0125] The providing unit can further estimate the user's emotions and adjust the timing of providing the advertisement image based on the estimated user's emotions. For example, if the user is relaxed, the providing unit displays the advertisement image slowly. For example, if the user is in a hurry, the providing unit displays the advertisement image quickly. For example, if the user is excited, the providing unit displays the advertisement image at a visually stimulating timing. This makes it possible to provide the advertisement image at the optimal timing according to the user's emotions.

[0126] The reception unit can further propose the optimal advertising location taking into consideration the user's advertising purpose. For example, if the user's goal is brand awareness, the reception unit proposes an advertising space with high visibility. For example, if the user's goal is sales promotion, the reception unit proposes an advertising space that attracts a large number of target demographics. For example, if the user's goal is event announcement, the reception unit proposes an advertising space around the event venue. This makes it possible to propose the optimal advertising location according to the user's advertising purpose.

[0127] The reception unit can further estimate the user's emotions and adjust the method for specifying the advertisement placement location based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes the steps for specifying the advertisement placement location. For example, if the user is relaxed, the reception unit provides detailed specification options and suggests a customizable specification method. For example, if the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly specify the advertisement placement location. This makes it possible to adjust the method for specifying the advertisement placement location according to the user's emotions.

[0128] The collection unit can further collect weather information around the advertising space. For example, the collection unit collects weather data around the advertising space and evaluates the visibility and effectiveness of the advertisement. For example, the collection unit proposes the optimal timing for placing an advertisement based on a weather forecast around the advertising space. For example, the collection unit analyzes weather data around the advertising space and reflects the data in the design and placement of the advertisement. This makes it possible to propose an optimal advertising plan that takes weather information into consideration.

[0129] The analysis unit can further estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is excited, the analysis unit prioritizes analyzing visually appealing advertising plans. For example, if the user is relaxed, the analysis unit prioritizes analyzing advertising plans that include detailed information. For example, if the user is stressed, the analysis unit prioritizes analyzing simple and easy-to-understand advertising plans. This makes it possible to adjust the analysis criteria based on the user's emotions.

[0130] The providing unit can further estimate the user's emotions and adjust the method of providing advertising images based on the estimated user's emotions. For example, when the user is excited, the providing unit provides a visually appealing method of providing advertising images. For example, when the user is relaxed, the providing unit provides a method of providing advertising images that includes detailed information. For example, when the user is stressed, the providing unit provides a simple and easy-to-understand method of providing advertising images. This makes it possible to adjust the method of providing advertising images based on the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives a user's designation of the location where the advertisement will be placed. For example, the user can select an advertising space on the wall of a specific building or at a bus stop. Step 2: The collection unit collects information about the advertising space based on the information received by the reception unit. The collection unit collects information such as the availability of the advertising space, price, past advertising performance, surrounding traffic volume, visibility, etc. Step 3: The analysis unit analyzes the information collected by the collection unit and proposes the optimal advertising plan. For example, the analysis unit uses AI to analyze the collected data and proposes the optimal advertising plan. Step 4: The generation unit generates an advertising image based on the advertising plan proposed by the analysis unit. For example, the generation unit generates the advertising image using AR technology. Step 5: The providing unit provides the advertisement image generated by the generating unit to the user. The providing unit displays the advertisement image by superimposing it on a real landscape using, for example, a smartphone or a tablet.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a designation of an advertisement placement location from a user; a collection unit that collects information about advertising spaces based on the information received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and proposes an optimal advertising plan; a generation unit that generates an advertisement image based on the advertisement plan proposed by the analysis unit; a providing unit that provides the advertisement image generated by the generating unit to a user. A system characterized by:

2. The collecting unit Collect information including advertising space availability, pricing, past advertising performance, surrounding traffic, and visibility 2. The system of claim 1.

3. The generation unit Generate advertising images using AR technology 2. The system of claim 1.

4. The providing unit Advertisement images are superimposed on real-world scenes using smartphones and tablets 2. The system of claim 1.

5. The providing unit Provide users with the ability to adjust the size and position of ads 2. The system of claim 1.

6. The providing unit Provide users with the ability to contact advertising agencies based on their preferred advertising plan 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the method for specifying ad placement based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past advertising history and suggest the optimal method of selection 2. The system of claim 1.

Images